Adaptive System Research Articles

Bases of the adaptive system of fodder production for dairy cattle

Bases of the adaptive system of fodder production for dairy cattle

Absolute counts of leukocyte subsets in peripheral blood in pregnancies complicated by preterm prelabour rupture of membranes

Background This study aimed to assess variations in the absolute counts of various leukocyte subsets in the peripheral blood of women with pregnancies affected by preterm prelabour rupture of membranes (PPROM), in relation to the presence of intra-amniotic inflammation (IAI). Methods The study included fifty-two women with singleton pregnancies experiencing PPROM. Absolute counts of different leukocyte subpopulations, such as granulocytes, monocytes, lymphocytes, T cells and their subsets, B cells and their subsets, and NK cells and their subsets, were measured in maternal peripheral blood samples using multicolour flow cytometry. IAI was identified by elevated concentrations of interleukin 6 (IL-6) in the amniotic fluid, which was collected through transabdominal amniocentesis. Results Women with IAI exhibited higher absolute counts of leukocytes (p = 0.003), granulocytes (p = 0.008), and monocytes (p = 0.009). However, the presence of IAI did not significantly affect the absolute counts of lymphocytes or their subpopulations. Conclusions The study found that IAI is associated with changes in the absolute counts of leukocytes from the innate immunity compartment in the peripheral blood of women with pregnancies complicated by PPROM. Conversely, it does not significantly alter the counts of cells from the adaptive immune system. The changes observed may reflect the natural, temporal, and localised characteristics of IAI.

Research on ground mobile robot trajectory tracking control based on MPC and ANFIS

This study focuses on the control strategy for a ground mobile robot (GMR) with independent three-axis six-wheel drive and four-wheel independent steering, performing double lane change trajectory tracking in complex scenarios. Initially, a dynamic model of the six-wheel independent drive and steering GMR was constructed. Utilizing Model Predictive Control (MPC) technology, the challenge of trajectory tracking at low speeds was effectively addressed. For high-speed conditions, by thoroughly analyzing the impact of the predictive time-domain, this study innovatively introduced an Adaptive Neuro-Fuzzy Inference System (ANFIS) to dynamically adjust the prediction horizon of the MPC. A novel trajectory tracking algorithm integrating MPC and ANFIS was developed, with the network structure being trained using backpropagation (BP) method and the least squares method. Compared to traditional MPC, this hybrid strategy significantly improves trajectory tracking accuracy and stability at high speeds, with computational efficiency increased by 48.65%. Additionally, the algorithm demonstrated excellent adaptability and control effectiveness in various rigorous tests, including different speed levels, complex steering paths, load changes, sudden obstacles, and variable terrain. A 70 km/h trajectory tracking experiment on a physical vehicle yielded a root mean square (RMS) error of 0.1904 m, verifying its superior tracking performance and practical reliability. This provides a pioneering solution for high-performance trajectory control of ground mobile robots.

A PM2.5 pollution-level adaptive air filtration system based on elastic filters for reducing energy consumption

Exacerbated by human activities and natural events, air pollution poses severe health risks, requiring effective control measures to ensure healthy living environments. Traditional filtration systems that employ high-efficiency particulate air (HEPA) filters are capable of effectively removing particulate matter (PM) in indoor environments. However, these systems often work without considering the fluctuations in air pollution levels, leading to high energy consumption. This study proposed a novel PM2.5 pollution-level adaptive air filtration system that combined elastic thermoplastic polyurethane (TPU) filters and an Internet of Things (IoT) system. The developed system can effectively adjust its filtration performance (i.e., pressure drop and PM2.5 filtration efficiency) in response to real-time air quality conditions by mechanically altering the structures of TPU filters. Furthermore, while operating in varied pollution conditions, the proposed system demonstrated remarkable reductions in pressure drop without notably compromising the pollution control capability. Finally, the energy consumption of the pollution-level adaptive air filtration system was estimated when applied in mechanical ventilation systems in different cities (Hong Kong, Beijing, and Xi’an) with various pollution conditions. The results revealed that, compared to a traditional fixed system, the annual energy consumption could be reduced by up to ∼26.4 % in Hong Kong.

Machine Psychology: integrating operant conditioning with the non-axiomatic reasoning system for advancing artificial general intelligence research.

This paper presents an interdisciplinary framework, Machine Psychology, which integrates principles from operant learning psychology with a particular Artificial Intelligence model, the Non-Axiomatic Reasoning System (NARS), to advance Artificial General Intelligence (AGI) research. Central to this framework is the assumption that adaptation is fundamental to both biological and artificial intelligence, and can be understood using operant conditioning principles. The study evaluates this approach through three operant learning tasks using OpenNARS for Applications (ONA): simple discrimination, changing contingencies, and conditional discrimination tasks. In the simple discrimination task, NARS demonstrated rapid learning, achieving 100% correct responses during training and testing phases. The changing contingencies task illustrated NARS's adaptability, as it successfully adjusted its behavior when task conditions were reversed. In the conditional discrimination task, NARS managed complex learning scenarios, achieving high accuracy by forming and utilizing complex hypotheses based on conditional cues. These results validate the use of operant conditioning as a framework for developing adaptive AGI systems. NARS's ability to function under conditions of insufficient knowledge and resources, combined with its sensorimotor reasoning capabilities, positions it as a robust model for AGI. The Machine Psychology framework, by implementing aspects of natural intelligence such as continuous learning and goal-driven behavior, provides a scalable and flexible approach for real-world applications. Future research should explore using enhanced NARS systems, more advanced tasks and applying this framework to diverse, complex tasks to further advance the development of human-level AI.

Interactive robots for personalised multimodal comedy experiments

This research proposes a novel system for personalising humour delivery using interactive robots with AI, natural language processing, and human-robot interaction technologies. The robots can adapt the multimodal content of jokes tactically now and strategically over time based on recording and analyzing each user’s subjective emotional reactions. The study evaluated pragmatic aspects of timing and framing jokes by having the robots try different “comeback tactics” and observing audience response. An ANOVA analysis revealed six distinct response categories. 78 % of participants positively rated the personalized robot comedy across the six scenarios. However, when both robot comedians took a negative tone, only 23 % approved (p < 0.001). Interestingly, audiences validated the robot comedian’s performance (89 % approval) more than the human comedian (54 % approval), a statistically significant difference (p < 0.01). These findings shed light on ideal characteristics for effective human-robot comedy interactions, highlighting the importance of topicality, maintaining a generally positive mood, and the unique strengths of robot performers. The study paves the way for further optimization of personalized, adaptive robot humour systems leveraging multimodal AI capabilities to enhance entertainment experiences tailored to individuals.

ANFIS-based control of resonant converters for optimized charging system of electric vehicle (EV) batteries

This paper presents a resonant converter-based Electric Vehicle (EV) battery charging module utilizing Proportional-Integral (PI) and Adaptive Neuro-Fuzzy Inference System (ANFIS) control for an optimized charging system. The EV charging module is integrated with a resonant converter comprising a full bridge, HFTF, and DBR. The module utilizes a Resonant Converter which reduces the switching loss incurred during converter operation at high frequency by offering ZCS or ZVS at the switching time. A standard PI Controller manages the duty ratios of the primary full bridge switches with tuned gains. The CC and CV controllers each have their own PI Controller for current and voltage, respectively. To enhance the performance of the EV System, the standard PI Controllers in both the CC and CV control systems are replaced with ANFIS Controllers which are trained as per the data generated by the CC and CV control using an optimization technique that controls the duty ratio of the switches. The proposed ANFIS-based and PI-based control strategy provides an adaptive and flexible approach to control the battery voltage and current by intelligent adjustment of Constant Current (CC) and Constant Voltage (CV) operation modes and the passive elements switching across specific ranges of State-Of-Charge (SOC) to enhance the performance and safety of the charging system. MATLAB Simulation results demonstrated that the proposed ANFIS-based control reduces current ripple content compared to PI-based control. The ANFIS Controller improves overall battery performance, reliability, and stability, which makes it a better choice for next-generation EV charging systems.

Enhancing efficiency in spaceborne phased array systems: MVDR algorithm and FPGA integration

Digital Beamforming (DBF) has garnered significant attention within the space community, driven by the heightened flexibility offered by satellites equipped with active antennas for diverse applications like spaceborne synthetic aperture radar (SAR) systems and communication services. This paper presents a comprehensive exploration encompassing analysis, design, and implementation of a Minimum Variance Distortionless Response (MVDR) algorithm tailored for application in a Digital Beamforming Receiver system. The described system leverages the capabilities of the high-speed Analog to Digital Converter (ADC) device EV12AQ600, space-qualified, and the high-performance FPGA XCKU085. These components are seamlessly integrated into the EV12AQ60X-ADX-EVM evaluation board, thoughtfully provided by Teledyne e2v Semiconductors. The design methodology outlined herein is driven by the overarching objective to minimize FPGA resource utilization and power consumption for spaceborne phased array systems. This objective is chiefly met through the implementation of a systolic array structure adept at processing both real and complex input samples. This innovative approach results in a substantial reduction in allocated resources compared to conventional architectures. Furthermore, the design incorporates the use of the CORDIC algorithm to compute the inverse of the correlation matrix, obviating the need for square root and division operations. This strategic utilization of algorithms enhances the system's efficiency in terms of resource utilization, computational load, and processing time. To validate the anticipated behavior of the adaptive beamforming system, various radio frequency (RF) input signals are applied to the system, which is physically instantiated on the EV12AQ60X-ADX-EVM evaluation board. The experimental results affirm the efficacy of the proposed design, underscoring its suitability for spaceborne applications.

Fundamental equations and hypotheses governing glomerular hemodynamics.

The glomerular filtration rate (GFR) is the outcome of glomerular hemodynamics, influenced by a series of parameters: renal plasma flow, resistances of afferent arterioles and efferent arterioles (EAs), hydrostatic pressures in the glomerular capillary and Bowman's capsule, and plasma colloid osmotic pressure in the glomerular capillary. Although mathematical models have been proposed to predict the GFR at both the single-nephron level and the two-kidney system level using these parameters, mathematical equations governing glomerular filtration have not been well-established because of two major problems. First, the two-kidney system-level models are simply extended from the equations at the single-nephron level, which is inappropriate in epistemology and methodology. Second, the role of EAs in maintaining the normal GFR is underappreciated. In this article, these two problems are concretely elaborated, which collectively shows the need for a shift in epistemology toward a more holistic and evolving way of thinking, as reflected in the concept of the complex adaptive system (CAS). Then, we illustrate eight fundamental mathematical equations and four hypotheses governing glomerular hemodynamics at both the single-nephron and two-kidney levels as the theoretical foundation of glomerular hemodynamics. This illustration takes two steps. The first step is to modify the existing equations in the literature and establish a new equation within the conventional paradigm of epistemology. The second step is to formulate four hypotheses through logical reasoning from the perspective of the CAS (beyond the conventional paradigm). Finally, we apply the new equation and hypotheses to comprehensively analyze glomerular hemodynamics under different conditions and predict the GFR. By doing so, some concrete issues are eliminated. Unresolved issues are discussed from the perspective of the CAS and a desinger's view. In summary, this article advances the theoretical study of glomerular dynamics by 1) clarifying the necessity of shifting to the CAS paradigm; 2) adding new knowledge/insights into the significant role of EAs in maintaining the normal GFR; 3) bridging the significant gap between research findings and physiology education; and 4) establishing a new and advanced foundation for physiology education.

A simplified model to understand the targeted disease-modifying anti-inflammatory drugs

This review presents a simplified model to understand better how disease-modifying anti-inflammatory drugs (DMAIDs) work in immune-mediated inflammatory diseases (IMIDs) with a focus on rheumatology, dermatology, and gastroenterology. In this model, IMIDs are listed on a spectrum from autoinflammatory to autoimmune characterised by the involvement of either mostly the innate or the adaptive immune system. DMAIDs specifically target these immune components and have shown efficacy in distinct IMIDs. DMAID classes include TNF blockers, IL-1 blockers, IL-6 receptor blockers, IL-17 blockers, IL-23 blockers, and janus kinase inhibitors.

Optimal fare and headway for a demand adaptive paired-line hybrid transit system in a rectangular area with elastic demand

Demand adaptive paired-line hybrid transit systems that integrate fixed- and flex-route transit have emerged in the last decade and attracted increasing attention because of their potential to improve accessibility for passengers. To facilitate the operation of such a hybrid transit system, this study develops a model to determine the optimal fare and headways associated with fixed- and flex-route transit along a rectangular corridor. Compared with existing literature, the novelty of this study lies in designing the fare structure while simultaneously considering demand elasticity and passenger behaviour. A continuous approximation modelling approach is employed to derive the agency’s and travellers’ cost components. Using these, a nonlinear programming optimisation model is formulated to minimise the total user cost subject to the agency’s nonnegative revenue constraints and passengers’ route choice behaviour, which is characterised as a path-size logit model. Numerical experiments are performed using a stylised network to examine the properties of the model, in which the solution is obtained by combining a brute force method that enumerates headway and fare combinations and an iterative method that determines equilibrated passenger choices. The results show that as potential demand density increases, fare and headway fluctuate and drop, while the percentage of passengers choosing to ride on flex-route transit increases. In addition, there may be an optimal maximum offset distance, which is defined as the width of the corridor to be covered by the transit system, when the potential demand density is low, leading to minimum user cost and maximum travel demand within the service area.

An Improved Longitudinal Driving Car-Following System Considering the Safe Time Domain Strategy.

Car-following models are crucial in adaptive cruise control systems, making them essential for developing intelligent transportation systems. This study investigates the characteristics of high-speed traffic flow by analyzing the relationship between headway distance and dynamic desired distance. Building upon the optimal velocity model theory, this paper proposes a novel traffic car-following computing system in the time domain by incorporating an absolutely safe time headway strategy and a relatively safe time headway strategy to adapt to the dynamic changes in high-speed traffic flow. The interpretable physical law of motion is used to compute and analyze the car-following behavior of the vehicle. Three different types of car-following behaviors are modeled, and the calculation relationship is optimized to reduce the number of parameters required in the model's adjustment. Furthermore, we improved the calculation of dynamic expected distance in the Intelligent Driver Model (IDM) to better suit actual road traffic conditions. The improved model was then calibrated through simulations that replicated changes in traffic flow. The calibration results demonstrate significant advantages of our new model in improving average traffic flow speed and vehicle speed stability. Compared to the classic car-following model IDM, our proposed model increases road capacity by 8.9%. These findings highlight its potential for widespread application within future intelligent transportation systems. This study optimizes the theoretical framework of car-following models and provides robust technical support for enhancing efficiency within high-speed transportation systems.

РОЛЬ ИММУННОГО ОТВЕТА В РАЗВИТИИ ГИПОКСИЧЕСКИХ ОСЛОЖНЕНИЙ РЕСПИРАТОРНЫХ ИНФЕКЦИЙ НИЖНИХ ДЫХАТЕЛЬНЫХ ПУТЕЙ У ДЕТЕЙ

Pneumonia is one of the most common severe infectious diseases in pediatric practice. Hypoxia is common in children with pneumonia and is one of the main predictors of poor outcome. The role of the central link in the cellular response to hypoxia is played by the family of hypoxia-inducible factors (HIF). HIF-1α is the key regulator of both T- and B-lymphocyte functions. The processes of their maturation are accompanied by the formation of circular DNA fragments, TREC (T-cell Receptor Excision Circle) and KREC (Kappa-deleting Recombination Excision Circle). TREC and KREC are unable to replicate and serve as markers for naive lymphocyte production. In its turn, HIF-1α can be used as a marker for cellular hypoxia. The use of TREC and KREC in assessing the immune system in patients with various immunological and infectious pathologies has already been described. At the same time, HIF-1α has so far been little studied as yet. The purpose of this research was to analyze the scientific bibliographical data on the HIF-1α assessment as a marker for hypoxia as well as the quantitative level of TREC and KREC as markers for the adaptive immune response; to study the bibliographical sources on the effect of hypoxia on the immune response, including as part of lower respiratory tract infections. Methods used: bibliographical research in Scopus, PubMed and Google Scholar with keywords “TREC,” “KREC,” “HIF,” “hypoxia,” “pneumonia,” “immune response,” that was then followed by the search results’ systematization, analysis and synthesis. Results: the analysis of bibliographical sources had allowed the Authors to conclude that there is a possible close connection between acute hypoxia and the formation of an adequate immune response in infectious pathologies. Research in this area is still extremely scarce. At the same time, the prospects for this area determine the need for further fundamental and clinical research. Conclusion: it is of a great scientific and clinical interest to be able to non-invasively assess the degree of acute hypoxia during an ongoing infectious disease by examining urinary HIF-1α levels. Particularly promising is the assessment of the HIF-1α level not only as a regulator of tissue adaptation to acute hypoxia, but also as a modulator of the immune response. Taking into consideration the analyzed data, both TREC and KREC appear to be the optimal markers characterizing the functional state of the adaptive immune system.

Immunoglobulin prophylaxis prevents hospital admissions for fever in pediatric acute lymphoblastic leukemia: results of a multicenter randomized trial.

Infections lead to substantial morbidity during treatment of acute lymphoblastic leukemia (ALL) in which the adaptive immune system gets severely affected, leading to declining serum immunoglobulin levels. The aim of this trial was to investigate whether intravenous immunoglobulin (IVIG) prophylaxis in pediatric patients with ALL prevents admissions for fever. This randomized controlled trial was a subtrial of the national Dutch multicenter ALL study. Patients aged 1-19 years with medium risk (MR) ALL were randomized into two groups receiving either IVIG prophylaxis (0.7 g/kg IVIG given every three weeks, starting day 22 after diagnosis) or well defined standard of care (control group). Between October 2012 until March 2019, 91 (51%) patients were randomly assigned to IVIG prophylaxis and 86 (49%) to the control arm. In the IVIG prophylaxis group there were 206 admissions for fever versus 271 in the control group (p=0.011). IVIG prophylaxis was not associated with bacteremia. However, IVIG prophylaxis was associated with significantly less admissions for fever with negative blood cultures compared to the control group (N=113 versus 200, p.

The Prognostic Role of the Neutrophil-to-Lymphocytes Ratio in the Most Frequent Cardiovascular Diseases: An Update.

Given the continuous changes in the world, with an increasing trend of unhealthy lifestyles, metabolic comorbidities, and increased susceptibility to cardiovascular diseases (CVDs), researchers change their attention to improve not only the therapeutic platform but also current CVD predictive and prognostic tools to improve disease outcomes. As CVD is characterized by an inflammatory paradigm involving, to some degree, the innate and adaptative immune systems, the neutrophil-to-lymphocyte ratio (NLR) emerged as a potential low-cost, rapidly available, and reliable inflammatory marker, with substantial recent evidence showing its potential utility in clinical practice. Thus, in this literature review, we will present an up-to-date discussion of the prognostic role of NLR in the most frequent CVDs, such as acute and chronic coronary disease, atherosclerotic disease, heart failure, cardiac valvopathies, and cardiac arrhythmias with predilection to atrial fibrillation.

Utilizing the ANFIS and teaching-learning-based optimization (TLBO) methods to predict the maximum depth of scouring at culvert outlets

ABSTRACT Excessive scouring at culvert outlets is a widespread issue that often leads to culvert failure. At present, there is an absence of a universally applicable model for accurately predicting the occurrence of local scouring at culvert outlets. In this paper, a comparison is made between the performance of existing empirical equations and the results obtained from two advanced methods: adaptive neuro-fuzzy inference system (ANFIS) and the teaching-learning-based optimization (TLBO). This paper presents the development of the TLBO method, based on the ANFIS approach, for simulating scouring at culvert outlets. The algorithms for supervised training are applied by utilizing data collected from published studies. The results illustrate that the ANFIS–TLBO model successfully predicts scouring depth at culvert outlets with higher accuracy in comparison to existing empirical formulas. Furthermore, the model exhibits a wider range of applicability, accommodating diverse conditions. By integrating the ANFIS approach and TLBO optimization, the model surpasses the limitations of traditional empirical equations, providing engineers with a more reliable tool for predicting scour depth at culvert outlets. Furthermore, the proposed model has the largest accuracy compared to the previously developed empirical models. Ultimately, it can be concluded that the hybrid ANFIS–TLBO is a robust approach for scour depth prediction downstream of culverts.

The current therapeutic cancer vaccines landscape in non-small cell lung cancer.

Currently, conventional immunotherapies for the treatment of non-small cell lung cancer (NSCLC) have low response rates and benefit only a minority of patients, particularly those with advanced disease, so novel therapeutic strategies are urgent deeded. Therapeutic cancer vaccines, a form of active immunotherapy, harness potential to activate the adaptive immune system against tumor cells via antigen cross-presentation. Cancer vaccines can establish enduring immune memory and guard against recurrences. Vaccine-induced tumor cell death prompts antigen epitope spreading, activating functional T cells and thereby sustaining a cancer-immunity cycle. The success of vaccines against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has rendered cancer vaccines a promising avenue, especially when combined with immunotherapy or chemoradiotherapy for NSCLC. This review delves into the intricate antitumor immune mechanisms underlying therapeutic cancer vaccines, enumerates the tumor antigen spectrum of NSCLC, discusses different cancer vaccines progress and summarizes relevant clinical trials. Additionally, we analyze the combination strategies, current limitations, and future prospects of cancer vaccines in NSCLC treatment, aiming to offer fresh insights for their clinical application in managing NSCLC. Overall, cancer vaccines offer promising potential for NSCLC treatment, particularly combining with chemoradiotherapy or immunotherapy could further improve survival in advanced patients. Exploring inhaled vaccines or prophylactic vaccines represents a crucial research avenue.

A Comprehensive Analysis Exploring the Impact of an Immunogenic Cell Death-Related Panel for Ovarian Cancer.

Ovarian cancer (OV) is a malignant tumor that ranks first among gynecological cancers, thus posing a significant threat to women's health. Immunogenic cell death (ICD) can regulate cell death by activating the adaptive immune system. Here, we aimed to comprehensively characterize the features of ICD-associated genes in ovarian cancer, and to investigate their prognostic value and role in the response to immunotherapy. After analyzing datasets from The Cancer Genome Atlas, we utilized weighted gene coexpressionnetwork analysis to screen for hub genes strongly correlated with ICD genes in OV, which was subsequently validated with OV samples from the Gene Expression Omnibus (GEO) database. A prognostic risk model was then constructed after combining univariate, multivariate Cox regression and LASSO regression analysis to recognize nine ICD-associated molecules. Next, we stratified all OV patients into two subgroups according to the median value. The multivariate Cox regression analysis showed that the risk model could predict OV patient survival with good accuracy. The same results were also found in the validation set from GEO. We then compared the degree of immune cell infiltration in the tumor microenvironment between the two subgroups of OV patients, and revealed that the high-risk subtype had a higher degree of immune infiltration than the low-risk subtype. Additionally, in contrast to patients in the high-risk subgroup, those in the low-risk subgroup were more susceptible to chemotherapy. In conclusion, our research offers an independent and validated model concerning ICD-related molecules to estimate the prognosis, degree of immune infiltration, and chemotherapy susceptibility inpatients with OV.

Adaptive neuro-fuzzy inference system approach for tensile properties prediction of LPDC A357 aluminum alloy

This study is based on the desire of aluminum casting foundries to understand the influence of minor changes, within the specification limits, in the alloy chemistry. In order to ensure the casting of A357 Al alloys within the framework of the casting standards and to minimize the quality problems that may arise during casting; the estimation of ultimate tensile strength (UTS), yield strength (YS) and elongation (ε) due to very small changes among the alloying elements, although they are in the standard range, by using machine learning method (ML), were studied. The dataset of chemical composition and tensile properties of Low-Pressure Die Cast (LPDC) A357 Al alloy were experimentally established. The relationship between five input variables in the A357 alloy, namely the main alloying elements Si and Mg together with the most common impurity contents Fe, Ti and Cu were selected and three outputs (i.e UTS, YS and ε) were linked by Adaptive Neuro Fuzzy Inference System (ANFIS). The ANFIS model predicted that the most detrimental element affecting tensile properties was Fe content. According to this model, the order of the relative importance on UTS, YS and ε revealed as Si, Mg and Ti content respectively after the Fe content of the alloy.

A Hierarchical Model for Complex Adaptive System: From Adaptive Agent to AI Society

As complex adaptive system involves human and social factors (e.g., changing demands, competition and collaboration among agents), accurately modeling the complex features of adaptive agents and AI society is crucial for the effective analysis and governance of complex adaptive systems. However, existing modeling methods struggle to accurately represent these complex features, there is a gap between existing technologies and complex features modeling. In this context, this paper proposes a hierarchical model based on the computational experiments method, which consists of four layers (i.e., L1, L2, L3 and L4) modeling the autonomous, evolutionary, interactive, and emergent features respectively from adaptive agent to AI society. Additionally, taking intelligent transportation system as an example, a computational experiments system is constructed to demonstrate the effectiveness of the proposed model. This model builds a bridge between complex feature modeling and various technologies, thereby offering theoretical support for further research in complex adaptive systems.